Phonological constraints can, in principle, be classified according to whether they are natural (founded in principles of universal grammar (UG)) or unnatural (arbitrary, learned inductively from the language data). Recent work has used this distinction as the basis for arguments about the role of UG in learning. Some languages have phonological patterns that arguably reflect unnatural constraints. With experimental testing, one can assess whether such patterns are actually learned by native speakers. Becker, Ketrez, and Nevins (2007), testing speakers of Turkish, suggest that they do indeed go unlearned. They interpret this result with a strong UG position: humans are unable to learn data patterns not backed by UG principles.

This article pursues the same research line, locating similarly unnatural data patterns in the vowel harmony system of Hungarian, such as the tendency (among certain stem types) for a final bilabial stop to favor front harmony. Our own test leads to the opposite conclusion of Becker and colleagues': Hungarians evidently do learn the unnatural patterns.

To conclude we consider a bias account—that speakers are able to learn unnatural environments, but devalue them relative to natural ones. We outline a method for testing the strength of constraints as learned by speakers against the strength of the corresponding patterns in the lexicon, and show that it offers tentative support for the hypothesis that unnatural constraints are disfavored by language learners.

I present evidence from Navajo and English that weaker, gradient versions of morpheme-internal phonotactic constraints, such as the ban on geminate consonants in English, hold even across prosodic word boundaries. I argue that these lexical biases are the result of a maximum entropy phonotactic learning algorithm that maximizes the probability of the learning data, but that also contains a smoothing term that penalizes complex grammars. When this learner attempts to construct a grammar in which some constraints are blind to morphological structure, it underpredicts the frequency of compounds that violate a morpheme-internal phonotactic. I further show how, over time, this learning bias could plausibly lead to the lexical biases seen in Navajo and English.

It is well known that any higher-order Markov chain can be associated with a first-order Markov chain. In this primarily expository article, we present the first fairly comprehensive analysis of the relationship between higher-order and first-order Markov chains, together with illustrative examples. Our main objective is to address the central question as posed in the title.

This article argues that identificational focus, which expresses exhaustive identification and occupies the specifier of a functional projection, must be distinguished in language description from information focus, which conveys new information and involves no syntactic reordering. The properties of the two types of focus are established on the basis of Hungarian and English material. It is argued that the cleft constituent is the realization of identificational focus in English. Only-phrases are analyzed as identificational foci carrying an evaluative presupposition. The feature specification of identificational focus is shown to be subject to parametric variation: the focus operators of various languages are specified for the positive value of either or both of the features [+exhaustive] and [+contrastive].

Health technology assessment (HTA) processes provide evidence to inform the supply of healthcare, often comparing results from economic evaluation to a policy threshold to judge cost-effectiveness. However, recommended policy thresholds may not always align with empirical estimates of the opportunity costs of health care expenditure, captured by marginal productivity of healthcare expenditure (‘k’). Such estimates are needed to inform the net health impact of funding decisions. We map policy thresholds in HTA guidelines against published estimates of k. We extract information from HTA guidelines identified in a previous literature review, including recommended perspective, relevant costs and outcomes, and justification for the threshold. Studies estimating k were obtained from a separate review. Of the 47 included HTA guidelines, 20 state an explicit policy threshold and 12 justify their choice. Estimates of k were available for 13 countries. Among the eight countries with explicit policy thresholds and k estimates, three matched. The recommended perspective influences whether k alone is sufficient or appropriate to inform cost-effectiveness judgements. It is important that guideline setters are aware of empirical estimates of k; and that economic evaluations consider k to reflect health opportunity costs even where the policy threshold is justified on other grounds.